Class MovingAverageConvergenceDivergence

Jump right in for a hands-on

Import

from NitroFE import MovingAverageConvergenceDivergence

MovingAverageConvergenceDivergence

= Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.

The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals.

\[ Moving \ average \ convergence \ divergence \ (MACD) = Exponential \ moving \ average \ of \ (Absolute \ Price \ Oscillator) \]

\[ Moving average \ convergence \ divergence \ histogram = (Absolute \ Price \ Oscillator)- (Exponential \ moving \ average \ of \ (Absolute \ Price)) \]

Methods

Provided dataframe must be in ascending order.

`init(self, fast_period=26, slow_period=12, smoothing_period=9, fast_operation='mean', slow_operation='mean', smoothing_operation='mean', initialize_using_operation=False, initialize_span=None, min_periods=0, ignore_na=False, axis=0, times=None, return_histogram=False)` `special`

Parameters:

Name	Type	Description	Default
`fast_period`	`int`	specify decay in terms of span, for the fast moving feature, by default 12	`26`
`slow_period`	`int`	specify decay in terms of span, for the slow moving feature, by default 26	`12`
`smoothing_period`	`int`	specify decay in terms of span, for the smoothing moving feature, by default 9	`9`
`fast_operation`	`str`	operation to be performed for the fast moving feature, by default 'mean'	`'mean'`
`slow_operation`	`str`	operation to be performed for the slow moving feature, by default 'mean'	`'mean'`
`smoothing_operation`	`str`	operation to be performed for the smoothing moving feature, by default 'mean'	`'mean'`
`initialize_using_operation`	`bool`	If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False	`False`
`initialize_span`	`int`	the span over which 'operation' would be performed for initialization, by default None	`None`
`min_periods`	`int`	Minimum number of observations in window required to have a value (otherwise result is NA), by default 0	`0`
`ignore_na`	`bool`	Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False	`False`
`axis`	`int`	The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0	`0`
`times`	`str`	Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None	`None`

Source code in nitrofe\time_based_features\indicator_features\_movingaverageconvergencedivergence.py

def __init__(
    self,
    fast_period: int = 26,
    slow_period: int = 12,
    smoothing_period: int = 9,
    fast_operation: str = "mean",
    slow_operation: str = "mean",
    smoothing_operation: str = "mean",
    initialize_using_operation: bool = False,
    initialize_span: int = None,
    min_periods: int = 0,
    ignore_na: bool = False,
    axis: int = 0,
    times: str = None,
    return_histogram=False,
):
    """

    Parameters
    ----------
    fast_period : int, optional
        specify decay in terms of span, for the fast moving feature, by default 12
    slow_period : int, optional
        specify decay in terms of span, for the slow moving feature, by default 26
    smoothing_period : int, optional
        specify decay in terms of span, for the smoothing moving feature, by default 9
    fast_operation : str, {'mean','var','std'}
        operation to be performed for the fast moving feature, by default 'mean'
    slow_operation : str, {'mean','var','std'}
        operation to be performed for the slow moving feature, by default 'mean'
    smoothing_operation : str, optional
        operation to be performed for the smoothing moving feature, by default 'mean'
    initialize_using_operation : bool, optional
        If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False
    initialize_span : int, optional
        the span over which 'operation' would be performed for initialization, by default None
    min_periods : int, optional
        Minimum number of observations in window required to have a value (otherwise result is NA), by default 0
    ignore_na : bool, optional
        Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False
    axis : int, optional
        The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0
    times : str, optional
        Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None
    """

    self.span_fast = fast_period
    self.span_slow = slow_period
    self.min_periods = min_periods
    self.ignore_na = ignore_na
    self.axis = axis
    self.times = times
    self.fast_operation = fast_operation
    self.slow_operation = slow_operation
    self.smoothing_operation = smoothing_operation
    self.smoothing_period = smoothing_period
    self.return_histogram = return_histogram

    self.initialize_using_operation = initialize_using_operation
    self.initialize_span = initialize_span

`fit(self, dataframe, first_fit=True)`

For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test implementation pass fit_first=False

Returns --> Smoothed signal line , macd histogram